Data Challenge Summer Fall 2022

• Deliverble 3
• Xiaohui Tan
• Oct 3rd, 2022

#!pip install pandasql
from pandasql import sqldf 
pysqldf = lambda q: sqldf(q, globals())
import pandas as pd
import numpy as np

scans = pd.read_excel('LAC Data Challenge Fall 2022 - Data.xlsx', sheet_name='Ticket Scan Data')

Qustion 1

q1 = """SELECT event_name, strftime('%H:%M',event_datetime) AS eve_time FROM scans;"""

sqldf("SELECT event_name, IIF(strftime('%H:%M',event_datetime) < strftime('%H:%M','18:00'),'afternoon','evening') AS game_time  FROM scans;")

	event_name	game_time
0	LA Clippers 2021-22 Game 2 - Portland 10/25/21	evening
1	LA Clippers 2021-22 Game 2 - Portland 10/25/21	evening
2	LA Clippers 2021-22 Game 2 - Portland 10/25/21	evening
3	LA Clippers 2021-22 Game 2 - Portland 10/25/21	evening
4	LA Clippers 2021-22 Game 2 - Portland 10/25/21	evening
...	...	...
236465	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	evening
236466	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	evening
236467	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	evening
236468	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	evening
236469	LA Clippers 2021-22 Game 33 - New York 3/6/22	evening

236470 rows × 2 columns

#This SQL code extract the average attendence by evening(after 18:00) and afternoon(before 18:00)
attendance_by_time = """
    SELECT 
        game_time, CAST(COUNT(game_time)/COUNT(DISTINCT event_name) AS int) AS average_attendence 
    FROM 
        (SELECT event_name, IIF(strftime('%H:%M',event_datetime) < strftime('%H:%M','18:00'),'afternoon','evening') AS game_time FROM scans) 
    GROUP BY game_time;"""

pysqldf(attendance_by_time)

	game_time	average_attendence
0	afternoon	5008
1	evening	5923

With the above table shown, we can conclude that the evening game has higher attendance than afternoon game.

sqldf("SELECT event_name, strftime('%m',event_datetime) AS month FROM scans")

	event_name	month
0	LA Clippers 2021-22 Game 2 - Portland 10/25/21	10
1	LA Clippers 2021-22 Game 2 - Portland 10/25/21	10
2	LA Clippers 2021-22 Game 2 - Portland 10/25/21	10
3	LA Clippers 2021-22 Game 2 - Portland 10/25/21	10
4	LA Clippers 2021-22 Game 2 - Portland 10/25/21	10
...	...	...
236465	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	04
236466	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	04
236467	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	04
236468	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	04
236469	LA Clippers 2021-22 Game 33 - New York 3/6/22	03

236470 rows × 2 columns

#This is the code report the average attendance per game by month. 
attendance_by_month = """
    SELECT month, 
        CAST(COUNT(month)/COUNT(DISTINCT event_name) AS int) AS average_attendance 
    FROM
        (SELECT event_name, strftime('%m',event_datetime) AS month FROM scans) 
    GROUP BY month; """

pysqldf(attendance_by_month)

	month	average_attendance
0	01	4414
1	02	6619
2	03	6555
3	04	6177
4	10	5700
5	11	5655
6	12	5559

pysqldf(attendance_by_month).plot.bar(x = 'month', y = 'average_attendance', rot=0)

<AxesSubplot:xlabel='month'>

Qustion 2

Note that number of fans in arena:

• 2hr prior to start count if ticket scan time <= 2 hrs prior event time.
• 1hr prior to start count if ticket scan time <= 1 hr prior event time.
• at start count if ticket scan time <= event time
• people who being lated are not counted in this question.

average_num_of_fan_atTime = """
    SELECT 
        AVG(twohrs_prior) AS 'avgFan_2hr_prior', 
        AVG(onehr_prior) AS 'avgFan_1hr_prior',
        AVG(at_start) AS 'avgFan_at_start' 
    FROM(SELECT 
            event_name, 
            COUNT(CASE WHEN datetime(scan_datetime, '-2 hour') <= datetime(event_datetime) THEN 1 ELSE NULL END) AS 'at_start', 
            COUNT(CASE WHEN datetime(scan_datetime, '-2 hour') <= datetime(event_datetime, '-1 hour') THEN 1 ELSE NULL END) AS 'onehr_prior',
            COUNT(CASE WHEN datetime(scan_datetime, '-2 hour') <= datetime(event_datetime, '-2 hour') THEN 1 ELSE NULL END) AS 'twohrs_prior' 
        FROM scans 
        GROUP BY event_name);
    """

pysqldf(average_num_of_fan_atTime)

	avgFan_2hr_prior	avgFan_1hr_prior	avgFan_at_start
0	2.073171	706.390244	3574.756098

Qustion 3

member = pd.read_excel('LAC Data Challenge Fall 2022 - Data.xlsx', sheet_name='Customer Package Type')
sales = pd.read_excel('LAC Data Challenge Fall 2022 - Data.xlsx', sheet_name='Ticket Sales Data')

a = """
    SELECT 
        customer_unique_id, 
        COUNT(DISTINCT seat_unique_id) AS 'ticket_purchase' 
    FROM sales 
    GROUP BY customer_unique_id;
    """

c = """
    SELECT COUNT(CASE WHEN ticket_purchase >= 5 THEN 1 ELSE NULL END) AS 'purchase>=5'
    FROM (
        SELECT COUNT(DISTINCT seat_unique_id) AS 'ticket_purchase'
        FROM sales 
        GROUP BY customer_unique_id);
    """

f = """
    SELECT customer_unique_id, COUNT(DISTINCT seat_unique_id) AS 'ticket_scanned' 
          FROM(
              SELECT sales.customer_unique_id, scans.seat_unique_id, sales.seat_unique_id
              FROM scans 
              JOIN sales ON scans.seat_unique_id = sales.seat_unique_id) 
    GROUP BY customer_unique_id;
    """

h = """
    SELECT COUNT(CASE WHEN ticket_scanned >= 3 THEN 1 ELSE NULL END) AS 'scanned>=3'
    FROM (
        SELECT COUNT(DISTINCT seat_unique_id) AS 'ticket_scanned'
        FROM (
            SELECT sales.customer_unique_id, scans.seat_unique_id, sales.seat_unique_id
            FROM scans 
            JOIN sales ON scans.seat_unique_id = sales.seat_unique_id)
        GROUP BY customer_unique_id);
    """

#Below is the complete code to show each customer_unique_id with purchase>=5 and scan >=3
i = """
    SELECT a.customer_unique_id, b.ticket_scanned, a.ticket_purchase
    FROM(
        SELECT customer_unique_id, COUNT(DISTINCT seat_unique_id) AS 'ticket_purchase' 
        FROM sales 
        GROUP BY customer_unique_id) a
    JOIN(
        SELECT customer_unique_id, COUNT(DISTINCT seat_unique_id) AS 'ticket_scanned' 
        FROM(
            SELECT sales.customer_unique_id, scans.seat_unique_id, sales.seat_unique_id
            FROM scans 
            JOIN sales ON scans.seat_unique_id = sales.seat_unique_id) 
        GROUP BY customer_unique_id) b 
    ON a.customer_unique_id = b.customer_unique_id
    WHERE b.ticket_scanned >= 3 AND a.ticket_purchase >= 5;
    """
pysqldf(i)

	customer_unique_id	ticket_scanned	ticket_purchase
0	124908643:10017873	36	40
1	124908643:10067883	42	46
2	124908643:10072874	32	40
3	124908643:10081887	62	80
4	124908643:10191875	56	82
...	...	...	...
3878	124908643:9748886	14	44
3879	124908643:9803914	78	82
3880	124908643:9829877	139	164
3881	124908643:9956873	16	24
3882	124908643:9992875	27	40

3883 rows × 3 columns

num_account_df = pysqldf(i)
num_account_df.head()

	customer_unique_id	ticket_scanned	ticket_purchase
0	124908643:10017873	36	40
1	124908643:10067883	42	46
2	124908643:10072874	32	40
3	124908643:10081887	62	80
4	124908643:10191875	56	82

#Count the member account with purchase >= 5 and scanned >=3
num_member = """
    SELECT COUNT(m.customer_unique_id) AS member_account 
    FROM member m
    JOIN num_account_df n 
    ON m.customer_unique_id = n.customer_unique_id;
    """
pysqldf(num_member)

	member_account
0	2411

With the above analysis, we can conclude that

• 3883 accounts has purchase >=5 and scans>=3,
• of those, 2411 accounts are members.

Question 4

seating = pd.read_excel('LAC Data Challenge Fall 2022 - Data.xlsx', sheet_name='Seating Chart')

#Calculate the capacity for each section:
cap = """
    SELECT section_name, SUM(seat_count) AS section_capacity 
    FROM seating
    GROUP BY section_name;
    """
capacity = pysqldf(cap)
capacity.head()

	section_name	section_capacity
0	101	388
1	102	393
2	103	204
3	104	126
4	105	254

#Calculate the attendance for each section by game on average:
sec = """
    SELECT section_name, SUM(section_att)/COUNT(DISTINCT event_name) AS 'avg_sec_att'
    FROM(
        SELECT event_name, section_name, COUNT(seat_unique_id) AS 'section_att'
        FROM scans
        GROUP BY event_name, section_name)
    GROUP BY section_name;
    """
att_by_section = pysqldf(sec)
att_by_section.head()

	section_name	avg_sec_att
0	101	273
1	102	292
2	103	139
3	104	86
4	105	187

#Merge 2 tables and calculate the fit capacity of each section
fit_capacity = capacity.merge(att_by_section, how = 'left', on = 'section_name')
fit_capacity['percentage_cap'] = fit_capacity['avg_sec_att']/fit_capacity['section_capacity']
fit_capacity.nlargest(n=10, columns = ['percentage_cap'])

	section_name	section_capacity	avg_sec_att	percentage_cap
5	106	502	374	0.745020
1	102	393	292	0.743003
4	105	254	187	0.736220
13	114	300	219	0.730000
6	107	473	345	0.729387
12	113	179	130	0.726257
14	115	478	346	0.723849
8	109	174	124	0.712644
0	101	388	273	0.703608
15	116	522	364	0.697318

Question 5

#Sales/attendance for each game 
sale_att = """
    SELECT a.event_name, date(b.event_datetime) AS 'event_date', game_sales, game_attendance
    FROM(
        SELECT event_name, COUNT(seat_unique_id) AS 'game_attendance'
        FROM scans
        GROUP BY event_name) a
    JOIN(
        SELECT event_name, event_datetime, COUNT(seat_unique_id) AS 'game_sales'
        FROM sales
        GROUP BY event_name) b
    ON a.event_name = b.event_name;
    """
sales_att_by_game = pysqldf(sale_att)
sales_att_by_game = sales_att_by_game.sort_values(by='event_date')

import re
#split the event_name column with only the city names
sales_att_by_game['event_name'].str.split("-", expand = True)[2]
cities = [item[0] for item in [re.split(r'(\d+)', s) for s in sales_att_by_game['event_name'].str.split("-", expand = True)[2]]]

#Clear the space in the city string
cities = [city.strip() for city in cities]
sales_att_by_game['City'] = cities
sales_att_by_game

	event_name	event_date	game_sales	game_attendance	City
0	LA Clippers 2021-22 Game 1 - Memphis 10/23/21	2021-10-23	7934	6498	Memphis
11	LA Clippers 2021-22 Game 2 - Portland 10/25/21	2021-10-25	7417	5738	Portland
22	LA Clippers 2021-22 Game 3 - Cleveland 10/27/21	2021-10-27	7044	4866	Cleveland
33	LA Clippers 2021-22 Game 4 - Oklahoma City 11/...	2021-11-01	7142	4784	Oklahoma City
36	LA Clippers 2021-22 Game 5 - Charlotte 11/7/21	2021-11-07	7838	6303	Charlotte
37	LA Clippers 2021-22 Game 6 - Portland 11/9/21	2021-11-09	7339	5531	Portland
38	LA Clippers 2021-22 Game 7 - Miami 11/11/21	2021-11-11	7340	6200	Miami
39	LA Clippers 2021-22 Game 8 - Minnesota 11/13/21	2021-11-13	7731	6045	Minnesota
40	LA Clippers 2021-22 Game 9 - Chicago 11/14/21	2021-11-14	7888	6789	Chicago
1	LA Clippers 2021-22 Game 10 - San Antonio 11/1...	2021-11-16	7126	4928	San Antonio
2	LA Clippers 2021-22 Game 11 - Dallas 11/21/21	2021-11-21	6832	5085	Dallas
3	LA Clippers 2021-22 Game 12 - Dallas 11/23/21	2021-11-23	7339	5903	Dallas
4	LA Clippers 2021-22 Game 13 - Detroit 11/26/21	2021-11-26	7477	5210	Detroit
5	LA Clippers 2021-22 Game 14 - Golden State 11/...	2021-11-28	7622	6832	Golden State
6	LA Clippers 2021-22 Game 15 - New Orleans11/29/21	2021-11-29	6713	4257	New Orleans
7	LA Clippers 2021-22 Game 16 - Sacramento 12/1/21	2021-12-01	6747	4558	Sacramento
8	LA Clippers 2021-22 Game 17 - Boston 12/8/21	2021-12-08	7456	6179	Boston
9	LA Clippers 2021-22 Game 18 - Orlando 12/11/21	2021-12-11	7361	4814	Orlando
10	LA Clippers 2021-22 Game 19 - Phoenix 12/13/21	2021-12-13	7678	6190	Phoenix
12	LA Clippers 2021-22 Game 20 - San Antonio 12/2...	2021-12-20	7835	5778	San Antonio
13	LA Clippers 2021-22 Game 21 - Denver 12/26/21	2021-12-26	7399	5566	Denver
14	LA Clippers 2021-22 Game 22 - Brooklyn 12/27/21	2021-12-27	7497	5829	Brooklyn
15	LA Clippers 2021-22 Game 23 - Minnesota 1/3/22	2022-01-03	7170	4587	Minnesota
16	LA Clippers 2021-22 Game 24 - Memphis 1/8/22	2022-01-08	7591	4850	Memphis
17	LA Clippers 2021-22 Game 25 - Atlanta 1/9/22	2022-01-09	6830	4053	Atlanta
18	LA Clippers 2021-22 Game 26 - Denver 1/11/22	2022-01-11	6921	4369	Denver
19	LA Clippers 2021-22 Game 27 - Indiana 1/17/22	2022-01-17	6901	4212	Indiana
20	LA Clippers 2021-22 Game 28 - Los Angeles 2/3/22	2022-02-03	7610	6913	Los Angeles
21	LA Clippers 2021-22 Game 29 - Milwaukee 2/6/22	2022-02-06	7678	6403	Milwaukee
23	LA Clippers 2021-22 Game 30 - Golden State 2/1...	2022-02-14	8033	7311	Golden State
24	LA Clippers 2021-22 Game 31 - Houston 2/17/22	2022-02-17	8006	5849	Houston
25	LA Clippers 2021-22 Game 32 - Los Angeles 3/3/22	2022-03-03	8073	7376	Los Angeles
26	LA Clippers 2021-22 Game 33 - New York 3/6/22	2022-03-06	7925	6198	New York
27	LA Clippers 2021-22 Game 34 - Washington 3/9/22	2022-03-09	7734	5629	Washington
28	LA Clippers 2021-22 Game 35 - Toronto 3/16/22	2022-03-16	8189	6774	Toronto
29	LA Clippers 2021-22 Game 36 - Philadelphia 3/2...	2022-03-25	8178	7162	Philadelphia
30	LA Clippers 2021-22 Game 37 - Utah 3/29/22	2022-03-29	7853	6192	Utah
31	LA Clippers 2021-22 Game 38 - New Orleans 4/3/22	2022-04-03	7586	6164	New Orleans
32	LA Clippers 2021-22 Game 39 - Phoenix 4/6/22	2022-04-06	7850	6631	Phoenix
34	LA Clippers 2021-22 Game 40 - Sacramento 4/9/22	2022-04-09	7685	5942	Sacramento
35	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	2022-04-10	7534	5972	Oklahoma City

team = pd.read_excel('LAC Data Challenge Fall 2022 - Data.xlsx', sheet_name='Team Data')
#Because only for the opponent teams, drop our home team
team1 = team.drop(12)

#Merge datasets
team2 = team1.merge(sales_att_by_game, on='City')
vegas_attendance = team2[["Team", "Vegas Over/Under 21/22", "game_attendance"]]

#Calculate the correlation
vegas_attendance['Vegas Over/Under 21/22'].corr(vegas_attendance['game_attendance'])

0.3690724620298187

#Make the best-fit plot
import matplotlib.pyplot as plt
import seaborn as sns
sns.lmplot(x='Vegas Over/Under 21/22', y='game_attendance', data=vegas_attendance)

<seaborn.axisgrid.FacetGrid at 0x7fd4794f2130>

Correlation Conclusion:

• The correlation between the opponent team’s Vegas over/under win total and attendance is 0.3690, it is low but still positive correlated

Question 6

#Code for scraping the Basketball Reference and pulling in data
url = 'https://www.basketball-reference.com/teams/LAC/2022_games.html'
d = pd.read_html(url)[0]
result = pd.DataFrame(d)

#rename the columns and drop the useless columns
result.rename( columns={'Unnamed: 5':'homeaway', 'Unnamed: 7':'W/L','Unnamed: 8': 'OT'}, inplace=True )
result = result[result['Opponent'] != 'Opponent']
del result['Unnamed: 3']
del result['Unnamed: 4']
result.head()

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	L	Streak	Notes
0	1	Thu, Oct 21, 2021	10:00p	@	Golden State Warriors	L	NaN	113	115	0	1	L 1	NaN
1	2	Sat, Oct 23, 2021	10:30p	NaN	Memphis Grizzlies	L	NaN	114	120	0	2	L 2	NaN
2	3	Mon, Oct 25, 2021	10:30p	NaN	Portland Trail Blazers	W	NaN	116	86	1	2	W 1	NaN
3	4	Wed, Oct 27, 2021	10:30p	NaN	Cleveland Cavaliers	L	NaN	79	92	1	3	L 1	NaN
4	5	Fri, Oct 29, 2021	10:00p	@	Portland Trail Blazers	L	NaN	92	111	1	4	L 2	NaN

#Modify the date format in order to merge the table with ticket purchase and attendance.
result1 = result

#Filter the 4 W3 Games
df_w3 = result1[(result1['Streak'] == "W 3")]
df_w3

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	L	Streak	Notes
7	8	Fri, Nov 5, 2021	8:00p	@	Minnesota Timberwolves	W	NaN	104	84	4	4	W 3	NaN
27	27	Sat, Dec 11, 2021	3:30p	NaN	Orlando Magic	W	NaN	106	104	15	12	W 3	NaN
65	63	Sun, Feb 27, 2022	7:00p	@	Houston Rockets	W	NaN	99	98	32	31	W 3	NaN
82	80	Wed, Apr 6, 2022	10:00p	NaN	Phoenix Suns	W	NaN	113	109	40	40	W 3	NaN

#Filter the 8 home games after W3 games
df_afterw3 = result1.filter(items = [8,9,10,28,29,30,66,67,68,83,84,85], axis=0)
df_8games = df_afterw3[(df_afterw3['homeaway'].isnull())]
df_8games

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	L	Streak	Notes
8	9	Sun, Nov 7, 2021	9:00p	NaN	Charlotte Hornets	W	NaN	120	106	5	4	W 4	NaN
9	10	Tue, Nov 9, 2021	10:00p	NaN	Portland Trail Blazers	W	NaN	117	109	6	4	W 5	NaN
10	11	Thu, Nov 11, 2021	10:30p	NaN	Miami Heat	W	NaN	112	109	7	4	W 6	NaN
28	28	Mon, Dec 13, 2021	10:30p	NaN	Phoenix Suns	W	NaN	111	95	16	12	W 4	NaN
67	65	Thu, Mar 3, 2022	10:00p	NaN	Los Angeles Lakers	W	NaN	132	111	34	31	W 5	NaN
68	66	Sun, Mar 6, 2022	10:00p	NaN	New York Knicks	L	NaN	93	116	34	32	L 1	NaN
84	81	Sat, Apr 9, 2022	9:30p	NaN	Sacramento Kings	W	NaN	117	98	41	40	W 4	NaN
85	82	Sun, Apr 10, 2022	9:30p	NaN	Oklahoma City Thunder	W	NaN	138	88	42	40	W 5	NaN

#Filter all the home games
df_homegames = result1[(result1['homeaway'].isnull()) & (result['Notes'] != 'Play-In Game')]
df_homegames.head()

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	L	Streak	Notes
1	2	Sat, Oct 23, 2021	10:30p	NaN	Memphis Grizzlies	L	NaN	114	120	0	2	L 2	NaN
2	3	Mon, Oct 25, 2021	10:30p	NaN	Portland Trail Blazers	W	NaN	116	86	1	2	W 1	NaN
3	4	Wed, Oct 27, 2021	10:30p	NaN	Cleveland Cavaliers	L	NaN	79	92	1	3	L 1	NaN
5	6	Mon, Nov 1, 2021	10:30p	NaN	Oklahoma City Thunder	W	NaN	99	94	2	4	W 1	NaN
8	9	Sun, Nov 7, 2021	9:00p	NaN	Charlotte Hornets	W	NaN	120	106	5	4	W 4	NaN

#Filter the 33 home games
df_8games['streak'] = 'streak'
df_8games.set_index('streak', append=True, inplace=True)
df_homegames['other'] = 'other'
df_homegames.set_index('other', append=True, inplace=True)
merged = df_8games.append(df_homegames)
merged = merged.drop_duplicates().sort_index()
idx = pd.IndexSlice
df_33games = merged.loc[idx[:, 'other'], :]
df_33games.index

df_33games = df_33games.reset_index(drop=True)
df_33games

<ipython-input-505-901245bade2d>:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-505-901245bade2d>:4: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	L	Streak	Notes
0	2	Sat, Oct 23, 2021	10:30p	NaN	Memphis Grizzlies	L	NaN	114	120	0	2	L 2	NaN
1	3	Mon, Oct 25, 2021	10:30p	NaN	Portland Trail Blazers	W	NaN	116	86	1	2	W 1	NaN
2	4	Wed, Oct 27, 2021	10:30p	NaN	Cleveland Cavaliers	L	NaN	79	92	1	3	L 1	NaN
3	6	Mon, Nov 1, 2021	10:30p	NaN	Oklahoma City Thunder	W	NaN	99	94	2	4	W 1	NaN
4	12	Sat, Nov 13, 2021	10:30p	NaN	Minnesota Timberwolves	W	NaN	129	102	8	4	W 7	NaN
5	13	Sun, Nov 14, 2021	9:30p	NaN	Chicago Bulls	L	NaN	90	100	8	5	L 1	NaN
6	14	Tue, Nov 16, 2021	10:30p	NaN	San Antonio Spurs	W	NaN	106	92	9	5	W 1	NaN
7	17	Sun, Nov 21, 2021	3:30p	NaN	Dallas Mavericks	W	NaN	97	91	10	7	W 1	NaN
8	18	Tue, Nov 23, 2021	10:30p	NaN	Dallas Mavericks	L	OT	104	112	10	8	L 1	NaN
9	19	Fri, Nov 26, 2021	3:30p	NaN	Detroit Pistons	W	NaN	107	96	11	8	W 1	NaN
10	20	Sun, Nov 28, 2021	3:30p	NaN	Golden State Warriors	L	NaN	90	105	11	9	L 1	NaN
11	21	Mon, Nov 29, 2021	10:30p	NaN	New Orleans Pelicans	L	NaN	104	123	11	10	L 2	NaN
12	22	Wed, Dec 1, 2021	10:30p	NaN	Sacramento Kings	L	NaN	115	124	11	11	L 3	NaN
13	26	Wed, Dec 8, 2021	10:30p	NaN	Boston Celtics	W	NaN	114	111	14	12	W 2	NaN
14	27	Sat, Dec 11, 2021	3:30p	NaN	Orlando Magic	W	NaN	106	104	15	12	W 3	NaN
15	31	Mon, Dec 20, 2021	10:30p	NaN	San Antonio Spurs	L	NaN	92	116	16	15	L 3	NaN
16	33	Sun, Dec 26, 2021	9:00p	NaN	Denver Nuggets	L	NaN	100	103	17	16	L 1	NaN
17	34	Mon, Dec 27, 2021	10:30p	NaN	Brooklyn Nets	L	NaN	108	124	17	17	L 2	NaN
18	38	Mon, Jan 3, 2022	10:30p	NaN	Minnesota Timberwolves	L	NaN	104	122	19	19	L 1	NaN
19	40	Sat, Jan 8, 2022	3:30p	NaN	Memphis Grizzlies	L	NaN	108	123	19	21	L 3	NaN
20	41	Sun, Jan 9, 2022	3:30p	NaN	Atlanta Hawks	W	NaN	106	93	20	21	W 1	NaN
21	42	Tue, Jan 11, 2022	10:30p	NaN	Denver Nuggets	W	NaN	87	85	21	21	W 2	NaN
22	45	Mon, Jan 17, 2022	3:30p	NaN	Indiana Pacers	W	NaN	139	133	22	23	W 1	NaN
23	54	Thu, Feb 3, 2022	10:00p	NaN	Los Angeles Lakers	W	NaN	111	110	27	27	W 1	NaN
24	55	Sun, Feb 6, 2022	9:00p	NaN	Milwaukee Bucks	L	NaN	113	137	27	28	L 1	NaN
25	59	Mon, Feb 14, 2022	10:30p	NaN	Golden State Warriors	W	NaN	119	104	29	30	W 2	NaN
26	61	Thu, Feb 17, 2022	10:30p	NaN	Houston Rockets	W	NaN	142	111	30	31	W 1	NaN
27	68	Wed, Mar 9, 2022	10:30p	NaN	Washington Wizards	W	NaN	115	109	35	33	W 1	NaN
28	72	Wed, Mar 16, 2022	10:30p	NaN	Toronto Raptors	L	NaN	100	103	36	36	L 2	NaN
29	75	Fri, Mar 25, 2022	10:30p	NaN	Philadelphia 76ers	L	NaN	97	122	36	39	L 5	NaN
30	76	Tue, Mar 29, 2022	10:00p	NaN	Utah Jazz	W	NaN	121	115	37	39	W 1	NaN
31	79	Sun, Apr 3, 2022	9:30p	NaN	New Orleans Pelicans	W	NaN	119	100	39	40	W 2	NaN
32	80	Wed, Apr 6, 2022	10:00p	NaN	Phoenix Suns	W	NaN	113	109	40	40	W 3	NaN

#Merge datasets with 8 games
from datetime import datetime
df_8games['event_date'] = df_8games['Date'].str.split(",", expand = True).drop(columns = 0)[1]+df_8games['Date'].str.split(",", expand = True).drop(columns = 0)[2]
df_8games['event_date'].astype(str)
df_8games['event_date'] = pd.to_datetime(df_8games['event_date']).astype(str)

#Merge datasets
#del df_8games['Date'] #Drop the useless column,already has event_date
new_df_8games = df_8games.merge(sales_att_by_game, on='event_date')
new_df_8games

<ipython-input-506-a125996dc749>:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-506-a125996dc749>:5: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	L	Streak	Notes	event_date	event_name	game_sales	game_attendance	City
0	9	Sun, Nov 7, 2021	9:00p	NaN	Charlotte Hornets	W	NaN	120	106	5	4	W 4	NaN	2021-11-07	LA Clippers 2021-22 Game 5 - Charlotte 11/7/21	7838	6303	Charlotte
1	10	Tue, Nov 9, 2021	10:00p	NaN	Portland Trail Blazers	W	NaN	117	109	6	4	W 5	NaN	2021-11-09	LA Clippers 2021-22 Game 6 - Portland 11/9/21	7339	5531	Portland
2	11	Thu, Nov 11, 2021	10:30p	NaN	Miami Heat	W	NaN	112	109	7	4	W 6	NaN	2021-11-11	LA Clippers 2021-22 Game 7 - Miami 11/11/21	7340	6200	Miami
3	28	Mon, Dec 13, 2021	10:30p	NaN	Phoenix Suns	W	NaN	111	95	16	12	W 4	NaN	2021-12-13	LA Clippers 2021-22 Game 19 - Phoenix 12/13/21	7678	6190	Phoenix
4	65	Thu, Mar 3, 2022	10:00p	NaN	Los Angeles Lakers	W	NaN	132	111	34	31	W 5	NaN	2022-03-03	LA Clippers 2021-22 Game 32 - Los Angeles 3/3/22	8073	7376	Los Angeles
5	66	Sun, Mar 6, 2022	10:00p	NaN	New York Knicks	L	NaN	93	116	34	32	L 1	NaN	2022-03-06	LA Clippers 2021-22 Game 33 - New York 3/6/22	7925	6198	New York
6	81	Sat, Apr 9, 2022	9:30p	NaN	Sacramento Kings	W	NaN	117	98	41	40	W 4	NaN	2022-04-09	LA Clippers 2021-22 Game 40 - Sacramento 4/9/22	7685	5942	Sacramento
7	82	Sun, Apr 10, 2022	9:30p	NaN	Oklahoma City Thunder	W	NaN	138	88	42	40	W 5	NaN	2022-04-10	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	7534	5972	Oklahoma City

#Merge datasets with 33 games
from datetime import datetime
df_33games['event_date'] = df_33games['Date'].str.split(",", expand = True).drop(columns = 0)[1]+df_33games['Date'].str.split(",", expand = True).drop(columns = 0)[2]
#result1['event_date'].astype(str)
df_33games['event_date'] = pd.to_datetime(df_33games['event_date']).astype(str)

#Merge datasets
#del df_33games['Date']
new_df_33games = df_33games.merge(sales_att_by_game, on='event_date')
new_df_33games

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	L	Streak	Notes	event_date	event_name	game_sales	game_attendance	City
0	2	Sat, Oct 23, 2021	10:30p	NaN	Memphis Grizzlies	L	NaN	114	120	0	2	L 2	NaN	2021-10-23	LA Clippers 2021-22 Game 1 - Memphis 10/23/21	7934	6498	Memphis
1	3	Mon, Oct 25, 2021	10:30p	NaN	Portland Trail Blazers	W	NaN	116	86	1	2	W 1	NaN	2021-10-25	LA Clippers 2021-22 Game 2 - Portland 10/25/21	7417	5738	Portland
2	4	Wed, Oct 27, 2021	10:30p	NaN	Cleveland Cavaliers	L	NaN	79	92	1	3	L 1	NaN	2021-10-27	LA Clippers 2021-22 Game 3 - Cleveland 10/27/21	7044	4866	Cleveland
3	6	Mon, Nov 1, 2021	10:30p	NaN	Oklahoma City Thunder	W	NaN	99	94	2	4	W 1	NaN	2021-11-01	LA Clippers 2021-22 Game 4 - Oklahoma City 11/...	7142	4784	Oklahoma City
4	12	Sat, Nov 13, 2021	10:30p	NaN	Minnesota Timberwolves	W	NaN	129	102	8	4	W 7	NaN	2021-11-13	LA Clippers 2021-22 Game 8 - Minnesota 11/13/21	7731	6045	Minnesota
5	13	Sun, Nov 14, 2021	9:30p	NaN	Chicago Bulls	L	NaN	90	100	8	5	L 1	NaN	2021-11-14	LA Clippers 2021-22 Game 9 - Chicago 11/14/21	7888	6789	Chicago
6	14	Tue, Nov 16, 2021	10:30p	NaN	San Antonio Spurs	W	NaN	106	92	9	5	W 1	NaN	2021-11-16	LA Clippers 2021-22 Game 10 - San Antonio 11/1...	7126	4928	San Antonio
7	17	Sun, Nov 21, 2021	3:30p	NaN	Dallas Mavericks	W	NaN	97	91	10	7	W 1	NaN	2021-11-21	LA Clippers 2021-22 Game 11 - Dallas 11/21/21	6832	5085	Dallas
8	18	Tue, Nov 23, 2021	10:30p	NaN	Dallas Mavericks	L	OT	104	112	10	8	L 1	NaN	2021-11-23	LA Clippers 2021-22 Game 12 - Dallas 11/23/21	7339	5903	Dallas
9	19	Fri, Nov 26, 2021	3:30p	NaN	Detroit Pistons	W	NaN	107	96	11	8	W 1	NaN	2021-11-26	LA Clippers 2021-22 Game 13 - Detroit 11/26/21	7477	5210	Detroit
10	20	Sun, Nov 28, 2021	3:30p	NaN	Golden State Warriors	L	NaN	90	105	11	9	L 1	NaN	2021-11-28	LA Clippers 2021-22 Game 14 - Golden State 11/...	7622	6832	Golden State
11	21	Mon, Nov 29, 2021	10:30p	NaN	New Orleans Pelicans	L	NaN	104	123	11	10	L 2	NaN	2021-11-29	LA Clippers 2021-22 Game 15 - New Orleans11/29/21	6713	4257	New Orleans
12	22	Wed, Dec 1, 2021	10:30p	NaN	Sacramento Kings	L	NaN	115	124	11	11	L 3	NaN	2021-12-01	LA Clippers 2021-22 Game 16 - Sacramento 12/1/21	6747	4558	Sacramento
13	26	Wed, Dec 8, 2021	10:30p	NaN	Boston Celtics	W	NaN	114	111	14	12	W 2	NaN	2021-12-08	LA Clippers 2021-22 Game 17 - Boston 12/8/21	7456	6179	Boston
14	27	Sat, Dec 11, 2021	3:30p	NaN	Orlando Magic	W	NaN	106	104	15	12	W 3	NaN	2021-12-11	LA Clippers 2021-22 Game 18 - Orlando 12/11/21	7361	4814	Orlando
15	31	Mon, Dec 20, 2021	10:30p	NaN	San Antonio Spurs	L	NaN	92	116	16	15	L 3	NaN	2021-12-20	LA Clippers 2021-22 Game 20 - San Antonio 12/2...	7835	5778	San Antonio
16	33	Sun, Dec 26, 2021	9:00p	NaN	Denver Nuggets	L	NaN	100	103	17	16	L 1	NaN	2021-12-26	LA Clippers 2021-22 Game 21 - Denver 12/26/21	7399	5566	Denver
17	34	Mon, Dec 27, 2021	10:30p	NaN	Brooklyn Nets	L	NaN	108	124	17	17	L 2	NaN	2021-12-27	LA Clippers 2021-22 Game 22 - Brooklyn 12/27/21	7497	5829	Brooklyn
18	38	Mon, Jan 3, 2022	10:30p	NaN	Minnesota Timberwolves	L	NaN	104	122	19	19	L 1	NaN	2022-01-03	LA Clippers 2021-22 Game 23 - Minnesota 1/3/22	7170	4587	Minnesota
19	40	Sat, Jan 8, 2022	3:30p	NaN	Memphis Grizzlies	L	NaN	108	123	19	21	L 3	NaN	2022-01-08	LA Clippers 2021-22 Game 24 - Memphis 1/8/22	7591	4850	Memphis
20	41	Sun, Jan 9, 2022	3:30p	NaN	Atlanta Hawks	W	NaN	106	93	20	21	W 1	NaN	2022-01-09	LA Clippers 2021-22 Game 25 - Atlanta 1/9/22	6830	4053	Atlanta
21	42	Tue, Jan 11, 2022	10:30p	NaN	Denver Nuggets	W	NaN	87	85	21	21	W 2	NaN	2022-01-11	LA Clippers 2021-22 Game 26 - Denver 1/11/22	6921	4369	Denver
22	45	Mon, Jan 17, 2022	3:30p	NaN	Indiana Pacers	W	NaN	139	133	22	23	W 1	NaN	2022-01-17	LA Clippers 2021-22 Game 27 - Indiana 1/17/22	6901	4212	Indiana
23	54	Thu, Feb 3, 2022	10:00p	NaN	Los Angeles Lakers	W	NaN	111	110	27	27	W 1	NaN	2022-02-03	LA Clippers 2021-22 Game 28 - Los Angeles 2/3/22	7610	6913	Los Angeles
24	55	Sun, Feb 6, 2022	9:00p	NaN	Milwaukee Bucks	L	NaN	113	137	27	28	L 1	NaN	2022-02-06	LA Clippers 2021-22 Game 29 - Milwaukee 2/6/22	7678	6403	Milwaukee
25	59	Mon, Feb 14, 2022	10:30p	NaN	Golden State Warriors	W	NaN	119	104	29	30	W 2	NaN	2022-02-14	LA Clippers 2021-22 Game 30 - Golden State 2/1...	8033	7311	Golden State
26	61	Thu, Feb 17, 2022	10:30p	NaN	Houston Rockets	W	NaN	142	111	30	31	W 1	NaN	2022-02-17	LA Clippers 2021-22 Game 31 - Houston 2/17/22	8006	5849	Houston
27	68	Wed, Mar 9, 2022	10:30p	NaN	Washington Wizards	W	NaN	115	109	35	33	W 1	NaN	2022-03-09	LA Clippers 2021-22 Game 34 - Washington 3/9/22	7734	5629	Washington
28	72	Wed, Mar 16, 2022	10:30p	NaN	Toronto Raptors	L	NaN	100	103	36	36	L 2	NaN	2022-03-16	LA Clippers 2021-22 Game 35 - Toronto 3/16/22	8189	6774	Toronto
29	75	Fri, Mar 25, 2022	10:30p	NaN	Philadelphia 76ers	L	NaN	97	122	36	39	L 5	NaN	2022-03-25	LA Clippers 2021-22 Game 36 - Philadelphia 3/2...	8178	7162	Philadelphia
30	76	Tue, Mar 29, 2022	10:00p	NaN	Utah Jazz	W	NaN	121	115	37	39	W 1	NaN	2022-03-29	LA Clippers 2021-22 Game 37 - Utah 3/29/22	7853	6192	Utah
31	79	Sun, Apr 3, 2022	9:30p	NaN	New Orleans Pelicans	W	NaN	119	100	39	40	W 2	NaN	2022-04-03	LA Clippers 2021-22 Game 38 - New Orleans 4/3/22	7586	6164	New Orleans
32	80	Wed, Apr 6, 2022	10:00p	NaN	Phoenix Suns	W	NaN	113	109	40	40	W 3	NaN	2022-04-06	LA Clippers 2021-22 Game 39 - Phoenix 4/6/22	7850	6631	Phoenix

new_df_8games['win3'] = 'Y'
new_df_33games['win3'] = 'N'

home_games = new_df_8games.append(new_df_33games, ignore_index=True)
home_games = home_games.sort_values(by="event_date")
home_games

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	L	Streak	Notes	event_date	event_name	game_sales	game_attendance	City	win3
8	2	Sat, Oct 23, 2021	10:30p	NaN	Memphis Grizzlies	L	NaN	114	120	0	2	L 2	NaN	2021-10-23	LA Clippers 2021-22 Game 1 - Memphis 10/23/21	7934	6498	Memphis	N
9	3	Mon, Oct 25, 2021	10:30p	NaN	Portland Trail Blazers	W	NaN	116	86	1	2	W 1	NaN	2021-10-25	LA Clippers 2021-22 Game 2 - Portland 10/25/21	7417	5738	Portland	N
10	4	Wed, Oct 27, 2021	10:30p	NaN	Cleveland Cavaliers	L	NaN	79	92	1	3	L 1	NaN	2021-10-27	LA Clippers 2021-22 Game 3 - Cleveland 10/27/21	7044	4866	Cleveland	N
11	6	Mon, Nov 1, 2021	10:30p	NaN	Oklahoma City Thunder	W	NaN	99	94	2	4	W 1	NaN	2021-11-01	LA Clippers 2021-22 Game 4 - Oklahoma City 11/...	7142	4784	Oklahoma City	N
0	9	Sun, Nov 7, 2021	9:00p	NaN	Charlotte Hornets	W	NaN	120	106	5	4	W 4	NaN	2021-11-07	LA Clippers 2021-22 Game 5 - Charlotte 11/7/21	7838	6303	Charlotte	Y
1	10	Tue, Nov 9, 2021	10:00p	NaN	Portland Trail Blazers	W	NaN	117	109	6	4	W 5	NaN	2021-11-09	LA Clippers 2021-22 Game 6 - Portland 11/9/21	7339	5531	Portland	Y
2	11	Thu, Nov 11, 2021	10:30p	NaN	Miami Heat	W	NaN	112	109	7	4	W 6	NaN	2021-11-11	LA Clippers 2021-22 Game 7 - Miami 11/11/21	7340	6200	Miami	Y
12	12	Sat, Nov 13, 2021	10:30p	NaN	Minnesota Timberwolves	W	NaN	129	102	8	4	W 7	NaN	2021-11-13	LA Clippers 2021-22 Game 8 - Minnesota 11/13/21	7731	6045	Minnesota	N
13	13	Sun, Nov 14, 2021	9:30p	NaN	Chicago Bulls	L	NaN	90	100	8	5	L 1	NaN	2021-11-14	LA Clippers 2021-22 Game 9 - Chicago 11/14/21	7888	6789	Chicago	N
14	14	Tue, Nov 16, 2021	10:30p	NaN	San Antonio Spurs	W	NaN	106	92	9	5	W 1	NaN	2021-11-16	LA Clippers 2021-22 Game 10 - San Antonio 11/1...	7126	4928	San Antonio	N
15	17	Sun, Nov 21, 2021	3:30p	NaN	Dallas Mavericks	W	NaN	97	91	10	7	W 1	NaN	2021-11-21	LA Clippers 2021-22 Game 11 - Dallas 11/21/21	6832	5085	Dallas	N
16	18	Tue, Nov 23, 2021	10:30p	NaN	Dallas Mavericks	L	OT	104	112	10	8	L 1	NaN	2021-11-23	LA Clippers 2021-22 Game 12 - Dallas 11/23/21	7339	5903	Dallas	N
17	19	Fri, Nov 26, 2021	3:30p	NaN	Detroit Pistons	W	NaN	107	96	11	8	W 1	NaN	2021-11-26	LA Clippers 2021-22 Game 13 - Detroit 11/26/21	7477	5210	Detroit	N
18	20	Sun, Nov 28, 2021	3:30p	NaN	Golden State Warriors	L	NaN	90	105	11	9	L 1	NaN	2021-11-28	LA Clippers 2021-22 Game 14 - Golden State 11/...	7622	6832	Golden State	N
19	21	Mon, Nov 29, 2021	10:30p	NaN	New Orleans Pelicans	L	NaN	104	123	11	10	L 2	NaN	2021-11-29	LA Clippers 2021-22 Game 15 - New Orleans11/29/21	6713	4257	New Orleans	N
20	22	Wed, Dec 1, 2021	10:30p	NaN	Sacramento Kings	L	NaN	115	124	11	11	L 3	NaN	2021-12-01	LA Clippers 2021-22 Game 16 - Sacramento 12/1/21	6747	4558	Sacramento	N
21	26	Wed, Dec 8, 2021	10:30p	NaN	Boston Celtics	W	NaN	114	111	14	12	W 2	NaN	2021-12-08	LA Clippers 2021-22 Game 17 - Boston 12/8/21	7456	6179	Boston	N
22	27	Sat, Dec 11, 2021	3:30p	NaN	Orlando Magic	W	NaN	106	104	15	12	W 3	NaN	2021-12-11	LA Clippers 2021-22 Game 18 - Orlando 12/11/21	7361	4814	Orlando	N
3	28	Mon, Dec 13, 2021	10:30p	NaN	Phoenix Suns	W	NaN	111	95	16	12	W 4	NaN	2021-12-13	LA Clippers 2021-22 Game 19 - Phoenix 12/13/21	7678	6190	Phoenix	Y
23	31	Mon, Dec 20, 2021	10:30p	NaN	San Antonio Spurs	L	NaN	92	116	16	15	L 3	NaN	2021-12-20	LA Clippers 2021-22 Game 20 - San Antonio 12/2...	7835	5778	San Antonio	N
24	33	Sun, Dec 26, 2021	9:00p	NaN	Denver Nuggets	L	NaN	100	103	17	16	L 1	NaN	2021-12-26	LA Clippers 2021-22 Game 21 - Denver 12/26/21	7399	5566	Denver	N
25	34	Mon, Dec 27, 2021	10:30p	NaN	Brooklyn Nets	L	NaN	108	124	17	17	L 2	NaN	2021-12-27	LA Clippers 2021-22 Game 22 - Brooklyn 12/27/21	7497	5829	Brooklyn	N
26	38	Mon, Jan 3, 2022	10:30p	NaN	Minnesota Timberwolves	L	NaN	104	122	19	19	L 1	NaN	2022-01-03	LA Clippers 2021-22 Game 23 - Minnesota 1/3/22	7170	4587	Minnesota	N
27	40	Sat, Jan 8, 2022	3:30p	NaN	Memphis Grizzlies	L	NaN	108	123	19	21	L 3	NaN	2022-01-08	LA Clippers 2021-22 Game 24 - Memphis 1/8/22	7591	4850	Memphis	N
28	41	Sun, Jan 9, 2022	3:30p	NaN	Atlanta Hawks	W	NaN	106	93	20	21	W 1	NaN	2022-01-09	LA Clippers 2021-22 Game 25 - Atlanta 1/9/22	6830	4053	Atlanta	N
29	42	Tue, Jan 11, 2022	10:30p	NaN	Denver Nuggets	W	NaN	87	85	21	21	W 2	NaN	2022-01-11	LA Clippers 2021-22 Game 26 - Denver 1/11/22	6921	4369	Denver	N
30	45	Mon, Jan 17, 2022	3:30p	NaN	Indiana Pacers	W	NaN	139	133	22	23	W 1	NaN	2022-01-17	LA Clippers 2021-22 Game 27 - Indiana 1/17/22	6901	4212	Indiana	N
31	54	Thu, Feb 3, 2022	10:00p	NaN	Los Angeles Lakers	W	NaN	111	110	27	27	W 1	NaN	2022-02-03	LA Clippers 2021-22 Game 28 - Los Angeles 2/3/22	7610	6913	Los Angeles	N
32	55	Sun, Feb 6, 2022	9:00p	NaN	Milwaukee Bucks	L	NaN	113	137	27	28	L 1	NaN	2022-02-06	LA Clippers 2021-22 Game 29 - Milwaukee 2/6/22	7678	6403	Milwaukee	N
33	59	Mon, Feb 14, 2022	10:30p	NaN	Golden State Warriors	W	NaN	119	104	29	30	W 2	NaN	2022-02-14	LA Clippers 2021-22 Game 30 - Golden State 2/1...	8033	7311	Golden State	N
34	61	Thu, Feb 17, 2022	10:30p	NaN	Houston Rockets	W	NaN	142	111	30	31	W 1	NaN	2022-02-17	LA Clippers 2021-22 Game 31 - Houston 2/17/22	8006	5849	Houston	N
4	65	Thu, Mar 3, 2022	10:00p	NaN	Los Angeles Lakers	W	NaN	132	111	34	31	W 5	NaN	2022-03-03	LA Clippers 2021-22 Game 32 - Los Angeles 3/3/22	8073	7376	Los Angeles	Y
5	66	Sun, Mar 6, 2022	10:00p	NaN	New York Knicks	L	NaN	93	116	34	32	L 1	NaN	2022-03-06	LA Clippers 2021-22 Game 33 - New York 3/6/22	7925	6198	New York	Y
35	68	Wed, Mar 9, 2022	10:30p	NaN	Washington Wizards	W	NaN	115	109	35	33	W 1	NaN	2022-03-09	LA Clippers 2021-22 Game 34 - Washington 3/9/22	7734	5629	Washington	N
36	72	Wed, Mar 16, 2022	10:30p	NaN	Toronto Raptors	L	NaN	100	103	36	36	L 2	NaN	2022-03-16	LA Clippers 2021-22 Game 35 - Toronto 3/16/22	8189	6774	Toronto	N
37	75	Fri, Mar 25, 2022	10:30p	NaN	Philadelphia 76ers	L	NaN	97	122	36	39	L 5	NaN	2022-03-25	LA Clippers 2021-22 Game 36 - Philadelphia 3/2...	8178	7162	Philadelphia	N
38	76	Tue, Mar 29, 2022	10:00p	NaN	Utah Jazz	W	NaN	121	115	37	39	W 1	NaN	2022-03-29	LA Clippers 2021-22 Game 37 - Utah 3/29/22	7853	6192	Utah	N
39	79	Sun, Apr 3, 2022	9:30p	NaN	New Orleans Pelicans	W	NaN	119	100	39	40	W 2	NaN	2022-04-03	LA Clippers 2021-22 Game 38 - New Orleans 4/3/22	7586	6164	New Orleans	N
40	80	Wed, Apr 6, 2022	10:00p	NaN	Phoenix Suns	W	NaN	113	109	40	40	W 3	NaN	2022-04-06	LA Clippers 2021-22 Game 39 - Phoenix 4/6/22	7850	6631	Phoenix	N
6	81	Sat, Apr 9, 2022	9:30p	NaN	Sacramento Kings	W	NaN	117	98	41	40	W 4	NaN	2022-04-09	LA Clippers 2021-22 Game 40 - Sacramento 4/9/22	7685	5942	Sacramento	Y
7	82	Sun, Apr 10, 2022	9:30p	NaN	Oklahoma City Thunder	W	NaN	138	88	42	40	W 5	NaN	2022-04-10	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	7534	5972	Oklahoma City	Y

#Make a plot to show the difference in quantity of attendance.
import plotly.express as px
import plotly.offline as pyo
pyo.init_notebook_mode()
fig1 = px.bar(data_frame = home_games, x = 'event_date', y = 'game_attendance', color = 'win3', barmode = 'group',
       template = 'plotly_white', labels={
                     'event_date': "The exact date of the game",
                     'game_attendance': "Number of attendance",
                 },
                title="Attendance in 8 home games after 3 win streak vs. other home games")
fig1.show()

#Make a plot to show the difference in quantity of ticket purchase.
import plotly.express as px
fig2 = px.bar(data_frame = home_games, x = 'event_date', y = 'game_sales', color = 'win3', barmode = 'group',
       template = 'plotly_white', labels={
                     'event_date': "The exact date of the game",
                     'game_sales': "Sales per game",
                 },
                title="Sales per game in 8 home games after 3 win streak vs. other home games")
fig2.show()

#Summary statistics between after W3 streak home games vs. other home games.
print(home_games.groupby('win3').describe())

     game_sales                                                            \
          count         mean         std     min     25%     50%      75%   
win3                                                                        
N          33.0  7475.454545  424.285067  6713.0  7142.0  7497.0  7835.00   
Y           8.0  7676.500000  265.266981  7339.0  7485.5  7681.5  7859.75   

             game_attendance                                                   \
         max           count         mean         std     min     25%     50%   
win3                                                                            
N     8189.0            33.0  5659.333333  939.821614  4053.0  4850.0  5778.0   
Y     8073.0             8.0  6214.000000  528.911281  5531.0  5964.5  6194.0   

                       
          75%     max  
win3                   
N     6403.00  7311.0  
Y     6225.75  7376.0  

Deliverable 3

Try to find interesting insight from the attendance and ticket sales between the top 3 oponent and the others

• The result shows that the different is huge. About 1500 differences between those top3 opponents and others
• As the result, I assume that the opponent is one of the most important factors that may affact ticket sales and attendance.

sales_att_by_game.head()

	event_name	event_date	game_sales	game_attendance	City
0	LA Clippers 2021-22 Game 1 - Memphis 10/23/21	2021-10-23	7934	6498	Memphis
11	LA Clippers 2021-22 Game 2 - Portland 10/25/21	2021-10-25	7417	5738	Portland
22	LA Clippers 2021-22 Game 3 - Cleveland 10/27/21	2021-10-27	7044	4866	Cleveland
33	LA Clippers 2021-22 Game 4 - Oklahoma City 11/...	2021-11-01	7142	4784	Oklahoma City
36	LA Clippers 2021-22 Game 5 - Charlotte 11/7/21	2021-11-07	7838	6303	Charlotte

#Select the averae attendance by oponent city
opp = """
    SELECT City AS 'opponent_city', AVG(game_attendance) AS 'avg_att_by_opponent'
    FROM sales_att_by_game
    GROUP BY City;
    """
average_att_by_opponent = pysqldf(opp)

#Select the top 3 opponent with highest attendance:
top3 = average_att_by_opponent.nlargest(3,['avg_att_by_opponent'])
top3

	opponent_city	avg_att_by_opponent
21	Philadelphia	7162.0
12	Los Angeles	7144.5
9	Golden State	7071.5

#select the other opponent
other = """
    SELECT AVG(avg_att_by_opponent) AS 'avg_att_by_opponent'
    FROM average_att_by_opponent
    WHERE opponent_city != 'Philadelphia' 
        AND opponent_city != 'Los Angeles'
        AND opponent_city != 'Golden State';
    """
pysqldf(other)

	avg_att_by_opponent
0	5622.615385

all = top3.append({'opponent_city': 'others', 'avg_att_by_opponent': 5622.6}, ignore_index=True)
all

	opponent_city	avg_att_by_opponent
0	Philadelphia	7162.0
1	Los Angeles	7144.5
2	Golden State	7071.5
3	others	5622.6

#px.bar(data_frame = all, x = 'opponent_city', y = 'avg_att_by_opponent', barmode = 'group',template = 'plotly_white')
ax = all.plot.bar(x = 'opponent_city', y = 'avg_att_by_opponent', rot=0)

sales_att_by_game.head()

	event_name	event_date	game_sales	game_attendance	City
0	LA Clippers 2021-22 Game 1 - Memphis 10/23/21	2021-10-23	7934	6498	Memphis
11	LA Clippers 2021-22 Game 2 - Portland 10/25/21	2021-10-25	7417	5738	Portland
22	LA Clippers 2021-22 Game 3 - Cleveland 10/27/21	2021-10-27	7044	4866	Cleveland
33	LA Clippers 2021-22 Game 4 - Oklahoma City 11/...	2021-11-01	7142	4784	Oklahoma City
36	LA Clippers 2021-22 Game 5 - Charlotte 11/7/21	2021-11-07	7838	6303	Charlotte

avg = """
    SELECT AVG(game_attendance) AS average_att_per_game
    FROM sales_att_by_game
    """
pysqldf(avg)

	average_att_per_game
0	5767.560976

Data Preparation & Feature Selection

Prepare and merge below datasets to built prediction model:

• 'home_games': All home games data with ticket_purchased and attendance
• 'average_att_by_opponent': Average attendance base on opponent
• 'team': Team data from original dataset

average_att_by_opponent.head()

	opponent_city	avg_att_by_opponent
0	Atlanta	4053.0
1	Boston	6179.0
2	Brooklyn	5829.0
3	Charlotte	6303.0
4	Chicago	6789.0

team2
#We select the variables that could be associated with the ticket sales and attendances. It is obvious those factors can also determine whether the team is good or not.
team1.rename(columns={"Team Full Name": "Opponent"}, inplace=True)
team3 = team1[["Opponent", "Conference", "Playoffs in 2020-2021", "Playoffs in 2021-2022", "Championships", "Vegas Over/Under 21/22", "Vegas Over/Under 22/23"]]

home_games.head()
#We merge the home games data with those variables of the team into a whole dataset
home_games1 = home_games.merge(team3, on='Opponent')
home_games1.columns

Index(['G', 'Date', 'Start (ET)', 'homeaway', 'Opponent', 'W/L', 'OT', 'Tm',
       'Opp', 'W', 'L', 'Streak', 'Notes', 'event_date', 'event_name',
       'game_sales', 'game_attendance', 'City', 'win3', 'Conference',
       'Playoffs in 2020-2021', 'Playoffs in 2021-2022', 'Championships',
       'Vegas Over/Under 21/22', 'Vegas Over/Under 22/23'],
      dtype='object')

We decided to use the variables that would be related to the home game attendance:

• "Conference","Playoffs in 2020-2021", "Playoffs in 2021-2022", "Championships", "Vegas Over/Under 21/22" ,"Vegas Over/Under 22/23", "Streak", "game_sales", "game_attendance" if opponent is in TOP3 attendance last season: 'Golden State', 'Los Angeles', 'Philadelphia' weekday or weekend column

#Convert yes or no to 1 or 0 , for convenience
home_games1["Conference"].replace(['Western', 'Eastern'],
                        [1, 0], inplace=True)
home_games1["Playoffs in 2020-2021"].replace(['Yes', 'No'],
                        [1, 0], inplace=True)
home_games1["Playoffs in 2021-2022"].replace(['Yes', 'No'],
                        [1, 0], inplace=True)

#Get dummy varibles from oponent variables:
dum = pd.get_dummies(home_games1['Opponent'])
dum.columns
#Select only the top three & insert columns to home_games1 dataframe
three = dum[['Golden State Warriors', 'Philadelphia 76ers', 'Los Angeles Lakers']]

home_games1['Golden State Warriors'] = three['Golden State Warriors']
home_games1['Philadelphia 76ers'] = three['Philadelphia 76ers']
home_games1['Los Angeles Lakers'] = three['Los Angeles Lakers']

#Check the unique value in streak variable
home_games1['Streak'].unique()

home_games1["Streak"].replace(['L 2', 'L 3', 'W 1', 'W 5', 'L 1', 'W 4', 'W 6', 'W 7', 'W 2',
       'W 3', 'L 5'],
                        [-2, -3, 1, 5, -1, 4, 6, 7, 2, 3, -5], inplace=True)

#Add weekday/weekend column
#home_games1['Date'].reset_index(drop=True).str.split(",", expand = True).head()
home_games1['weekday'] = home_games1['Date'].reset_index(drop=True).str.split(",", expand = True).drop(columns = 2)[0]

def get_days(a):
    if a in ['Sat','Sun']:
        return 1
    else:
        return 0

#Convert to weekend:1 weekday:0 
home_games1['weekend'] = home_games1['weekday'].apply(get_days)

#home_games1["weekday"] = home_games1["weekday"].replace(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat','Sun'],
                #[1, 2, 3, 4, 5, 6, 7], inplace=True)
#home_games1["weekday"] = home_games1["weekday"].astype(object)

home_games1.head()

	G	Date	Start (ET)	homeaway	Opponent	W/L	OT	Tm	Opp	W	...	Playoffs in 2020-2021	Playoffs in 2021-2022	Championships	Vegas Over/Under 21/22	Vegas Over/Under 22/23	Golden State Warriors	Philadelphia 76ers	Los Angeles Lakers	weekday	weekend
0	2	Sat, Oct 23, 2021	10:30p	NaN	Memphis Grizzlies	L	NaN	114	120	0	...	1	1	0	41.5	48.5	0	0	0	Sat	1
1	40	Sat, Jan 8, 2022	3:30p	NaN	Memphis Grizzlies	L	NaN	108	123	19	...	1	1	0	41.5	48.5	0	0	0	Sat	1
2	3	Mon, Oct 25, 2021	10:30p	NaN	Portland Trail Blazers	W	NaN	116	86	1	...	1	0	1	44.5	39.5	0	0	0	Mon	0
3	10	Tue, Nov 9, 2021	10:00p	NaN	Portland Trail Blazers	W	NaN	117	109	6	...	1	0	1	44.5	39.5	0	0	0	Tue	0
4	4	Wed, Oct 27, 2021	10:30p	NaN	Cleveland Cavaliers	L	NaN	79	92	1	...	0	0	1	26.5	47.5	0	0	0	Wed	0

5 rows × 30 columns

#Add Event Month column: 
g = """
    SELECT *, strftime('%m',event_date) AS month
    FROM home_games1
    """
home_games1 = pysqldf(g)
home_games1

#Convert to factor:

home_games1["month"] = home_games1["month"].astype(object)

#We decide to select the variables that are related to the attendance of the game.
#The reason for not selecting "Vegas Over/Under 22/23" is because we are now predicting 2021-2022 season attendance
home_games2122 = home_games1[[ "Conference","Playoffs in 2020-2021", "Championships", "game_sales", "Vegas Over/Under 21/22" , "Streak", "game_attendance",
                             'Golden State Warriors', 'Philadelphia 76ers', 'Los Angeles Lakers','weekend','month']]

#home_games2122.dtypes

home_games2122[home_games2122.columns[0:]].corr()['game_attendance'].sort_values()

Conference               -0.006147
weekend                   0.057169
Streak                    0.117491
Playoffs in 2020-2021     0.185885
Philadelphia 76ers        0.248776
Golden State Warriors     0.333180
Los Angeles Lakers        0.351833
Vegas Over/Under 21/22    0.400282
Championships             0.440146
game_sales                0.858419
game_attendance           1.000000
Name: game_attendance, dtype: float64

home_games2122[home_games2122.columns[0:]].corr()['game_sales'].sort_values()

Conference               -0.078228
Streak                    0.024697
Playoffs in 2020-2021     0.081492
weekend                   0.090679
Vegas Over/Under 21/22    0.132727
Golden State Warriors     0.177730
Los Angeles Lakers        0.185684
Championships             0.215935
Philadelphia 76ers        0.263135
game_attendance           0.858419
game_sales                1.000000
Name: game_sales, dtype: float64

import seaborn as sns
#correlation graph
sns.heatmap(home_games2122.corr(), annot=True , cmap="Blues").set(title='Heatmap of Home-game factors - Pearson Correlations');

Prediction Model

home_games2122.head()

	Conference	Playoffs in 2020-2021	Championships	game_sales	Vegas Over/Under 21/22	Streak	game_attendance	Golden State Warriors	Philadelphia 76ers	Los Angeles Lakers	weekend	month
0	1	1	0	7934	41.5	-2	6498	0	0	0	1	10
1	1	1	0	7591	41.5	-3	4850	0	0	0	1	01
2	1	1	1	7417	44.5	1	5738	0	0	0	0	10
3	1	1	1	7339	44.5	5	5531	0	0	0	0	11
4	0	0	1	7044	26.5	-1	4866	0	0	0	0	10

#Predict 21-22 regular season game attendance
#split the train and validation data
from sklearn.model_selection import train_test_split
X = home_games2122.drop(home_games2122.columns[[6]],axis = 1)
y = home_games2122['game_attendance']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

#Build the linear regression model
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm

reg = LinearRegression().fit(X_train, y_train)
y_pred = reg.predict(X_val)
reg.score(X_train,y_train) #Return the coefficient of determination of the prediction R^2.

0.9280606546854155

#See how good is the result
predscompare = pd.DataFrame({'Actual': y_val, 'Predicted': y_pred})
print(predscompare)

from sklearn import metrics
print({'mean_absolute error': metrics.mean_absolute_error(y_val, y_pred), 'root mean squared error': np.sqrt(metrics.mean_squared_error(y_val, y_pred))})

    Actual    Predicted
24    4814  5106.661901
13    5778  6275.125813
8     6200  5732.591025
25    6190  6500.953298
4     4866  4468.945264
40    6192  6420.870422
19    4257  4397.928053
39    7162  6809.805055
29    5829  6101.028571
{'mean_absolute error': 328.8029682725553, 'root mean squared error': 345.7494015487711}

importance = reg.coef_
intercept = reg.intercept_

coefs = pd.DataFrame(
   reg.coef_,
   columns=['Coefficients'], index=X_train.columns
)

coefs.plot(kind='barh', figsize=(9, 7))
plt.title('Linear model')
plt.axvline(x=0, color='.5')
plt.subplots_adjust(left=.3)

for i,v in enumerate(importance):
    print('Feature: %0d, Coefficient: %.5f' % (i,v))

Feature: 0, Coefficient: 118.59845
Feature: 1, Coefficient: -231.51631
Feature: 2, Coefficient: 26.95415
Feature: 3, Coefficient: 1.85317
Feature: 4, Coefficient: 32.81536
Feature: 5, Coefficient: 18.32986
Feature: 6, Coefficient: 244.36717
Feature: 7, Coefficient: 0.00000
Feature: 8, Coefficient: 271.29478
Feature: 9, Coefficient: 50.22130
Feature: 10, Coefficient: 42.46817

#Print the summary of the model result:
import statsmodels.api as sm
X = sm.add_constant(X)
stat = sm.OLS(y, X).fit()
stat.summary()

OLS Regression Results

Dep. Variable:	game_attendance	R-squared:	0.891
Model:	OLS	Adj. R-squared:	0.855
Method:	Least Squares	F-statistic:	24.56
Date:	Sat, 01 Oct 2022	Prob (F-statistic):	9.00e-12
Time:	22:16:03	Log-Likelihood:	-290.98
No. Observations:	41	AIC:	604.0
Df Residuals:	30	BIC:	622.8
Df Model:	10
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
const	-8084.8171	1172.236	-6.897	0.000	-1.05e+04	-5690.793
Conference	66.4609	124.811	0.532	0.598	-188.437	321.359
Playoffs in 2020-2021	-188.8087	177.977	-1.061	0.297	-552.287	174.670
Championships	41.4839	20.419	2.032	0.051	-0.218	83.185
Vegas Over/Under 21/22	29.1550	9.439	3.089	0.004	9.877	48.433
Streak	35.9768	21.602	1.665	0.106	-8.140	80.093
game_sales	1.6660	0.149	11.196	0.000	1.362	1.970
Golden State Warriors	294.1041	335.546	0.876	0.388	-391.172	979.380
Philadelphia 76ers	394.1528	399.817	0.986	0.332	-422.382	1210.688
Los Angeles Lakers	-56.0966	425.075	-0.132	0.896	-924.216	812.023
weekend	65.4311	118.071	0.554	0.584	-175.703	306.565

Omnibus:	1.992	Durbin-Watson:	2.081
Prob(Omnibus):	0.369	Jarque-Bera (JB):	1.531
Skew:	-0.473	Prob(JB):	0.465
Kurtosis:	2.955	Cond. No.	1.68e+05

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.68e+05. This might indicate that there are
strong multicollinearity or other numerical problems.

#game_table.join(predscompare)

#univariate regression graph for each features and game attendance variable

variables = ["Conference", "Playoffs in 2020-2021", "Championships", "Vegas Over/Under 21/22" , "Streak", "game_sales",
            'Golden State Warriors', 'Philadelphia 76ers', 'Los Angeles Lakers']

for var in variables:
    plt.figure() # Creating a rectangle (figure) for each plot
    # Regression Plot also by default includes
    # best-fitting regression line
    # which can be turned off via `fit_reg=False`
    sns.regplot(x=var, y='game_attendance', data= home_games2122).set(title=f'Regression plot of {var} and Game Attendance');

#Fit model to predict 22-23 home games attendance, replace the X variables with 22/23's data
home_games2223 = home_games1[["Conference", "Playoffs in 2021-2022", "Championships", "Vegas Over/Under 22/23" , "Streak", "game_sales", "game_attendance",
                             'Golden State Warriors', 'Philadelphia 76ers', 'Los Angeles Lakers','weekend','month']]

x_val = home_games2223.drop(home_games2122.columns[[6]],axis = 1)
y_ = reg.predict(x_val)
y_

array([6732.22472808, 5696.04371949, 5742.03588642, 5713.27617977,
       5158.06789553, 4749.83626484, 5302.54277123, 6425.90116534,
       5732.59102541, 6563.46797473, 4902.29722131, 6554.01119425,
       4795.18678523, 6078.23363238, 4814.44238102, 5667.11897009,
       5502.85052857, 6746.32580611, 7130.53375133, 4330.48855903,
       5774.57023618, 4315.13614639, 5892.19533981, 6458.14254558,
       5237.92335451, 6533.76866179, 6494.43880466, 5876.85990197,
       4528.66263174, 5904.13639066, 4169.00977086, 3813.8502244 ,
       6659.40591052, 7633.21162463, 6002.96878507, 5995.7164677 ,
       6308.08674027, 5782.35392574, 6667.19439307, 6809.80505513,
       5502.04024589])

home_games1.columns

Index(['G', 'Date', 'Start (ET)', 'homeaway', 'Opponent', 'W/L', 'OT', 'Tm',
       'Opp', 'W', 'L', 'Streak', 'Notes', 'event_date', 'event_name',
       'game_sales', 'game_attendance', 'City', 'win3', 'Conference',
       'Playoffs in 2020-2021', 'Playoffs in 2021-2022', 'Championships',
       'Vegas Over/Under 21/22', 'Vegas Over/Under 22/23',
       'Golden State Warriors', 'Philadelphia 76ers', 'Los Angeles Lakers',
       'weekday', 'weekend', 'month'],
      dtype='object')

The prediction result of each game using the 22/23 data

predict_att = pd.DataFrame({'Predicted': y_})
predict_2223 = home_games1[['event_name']].join(predict_att)
predict_2223

	event_name	Predicted
0	LA Clippers 2021-22 Game 1 - Memphis 10/23/21	6732.224728
1	LA Clippers 2021-22 Game 24 - Memphis 1/8/22	5696.043719
2	LA Clippers 2021-22 Game 2 - Portland 10/25/21	5742.035886
3	LA Clippers 2021-22 Game 6 - Portland 11/9/21	5713.276180
4	LA Clippers 2021-22 Game 3 - Cleveland 10/27/21	5158.067896
5	LA Clippers 2021-22 Game 4 - Oklahoma City 11/...	4749.836265
6	LA Clippers 2021-22 Game 41 - Oklahoma City 4/...	5302.542771
7	LA Clippers 2021-22 Game 5 - Charlotte 11/7/21	6425.901165
8	LA Clippers 2021-22 Game 7 - Miami 11/11/21	5732.591025
9	LA Clippers 2021-22 Game 8 - Minnesota 11/13/21	6563.467975
10	LA Clippers 2021-22 Game 23 - Minnesota 1/3/22	4902.297221
11	LA Clippers 2021-22 Game 9 - Chicago 11/14/21	6554.011194
12	LA Clippers 2021-22 Game 10 - San Antonio 11/1...	4795.186785
13	LA Clippers 2021-22 Game 20 - San Antonio 12/2...	6078.233632
14	LA Clippers 2021-22 Game 11 - Dallas 11/21/21	4814.442381
15	LA Clippers 2021-22 Game 12 - Dallas 11/23/21	5667.118970
16	LA Clippers 2021-22 Game 13 - Detroit 11/26/21	5502.850529
17	LA Clippers 2021-22 Game 14 - Golden State 11/...	6746.325806
18	LA Clippers 2021-22 Game 30 - Golden State 2/1...	7130.533751
19	LA Clippers 2021-22 Game 15 - New Orleans11/29/21	4330.488559
20	LA Clippers 2021-22 Game 38 - New Orleans 4/3/22	5774.570236
21	LA Clippers 2021-22 Game 16 - Sacramento 12/1/21	4315.136146
22	LA Clippers 2021-22 Game 40 - Sacramento 4/9/22	5892.195340
23	LA Clippers 2021-22 Game 17 - Boston 12/8/21	6458.142546
24	LA Clippers 2021-22 Game 18 - Orlando 12/11/21	5237.923355
25	LA Clippers 2021-22 Game 19 - Phoenix 12/13/21	6533.768662
26	LA Clippers 2021-22 Game 39 - Phoenix 4/6/22	6494.438805
27	LA Clippers 2021-22 Game 21 - Denver 12/26/21	5876.859902
28	LA Clippers 2021-22 Game 26 - Denver 1/11/22	4528.662632
29	LA Clippers 2021-22 Game 22 - Brooklyn 12/27/21	5904.136391
30	LA Clippers 2021-22 Game 25 - Atlanta 1/9/22	4169.009771
31	LA Clippers 2021-22 Game 27 - Indiana 1/17/22	3813.850224
32	LA Clippers 2021-22 Game 28 - Los Angeles 2/3/22	6659.405911
33	LA Clippers 2021-22 Game 32 - Los Angeles 3/3/22	7633.211625
34	LA Clippers 2021-22 Game 29 - Milwaukee 2/6/22	6002.968785
35	LA Clippers 2021-22 Game 31 - Houston 2/17/22	5995.716468
36	LA Clippers 2021-22 Game 33 - New York 3/6/22	6308.086740
37	LA Clippers 2021-22 Game 34 - Washington 3/9/22	5782.353926
38	LA Clippers 2021-22 Game 35 - Toronto 3/16/22	6667.194393
39	LA Clippers 2021-22 Game 36 - Philadelphia 3/2...	6809.805055
40	LA Clippers 2021-22 Game 37 - Utah 3/29/22	5502.040246